Optimizing the powder mixed EDM process of nickel based super alloy

Abstract

Powder Mixed Electric Discharge Machining has been the main focus of many researchers for quite a long time. The performance of EDM process has been improved significantly by using powder mixed dielectric medium. Numerous researches were undertaken to study the influence of different process parameters involved in the powder mixed EDM by experimenting with different work materials and powder combination. The objective of this work is to conduct experimental study on the powder Mixed EDM of a Nickel based super alloy, Nimonic 75, using three different powders which are graphite, silicon and manganese. The other important process parameters considered are concentration of powder (Cp), peak current (Ip), duty factor (DC). The experiments were conducted based on the Response Surface Methodology (RSM) design. The important measures of performance of EDM process chosen in this study are Material Removal Rate (MRR), Electrode Wear ratio (EWR) and Surface Roughness (SR). Graphite powder yielded highest MRR and silicon powder produced good finish of the components. The influence of powder at higher concentration is not significantly high. Moreover, Higher duty cycle caused the decrease of MRR, EWR and increase of SR. This works also aims to optimize the process parameters using Grey Relational Analysis (GRA) combined with Fuzzy logic method. The optimum conditions identified based on GFRG was powder = Mn; concentration = 3 g/l; discharge current = 4 A and duty cycle = 0.9 It was also estimated that the error in predicting best output of the PMEDM process is below 6%.

Role of biodegradable dielectrics toward tool wear and dimensional accuracy in Cu-mixed die sinking EDM of Inconel 600 for sustainable machining

“…The reason is linked with the reduction of Cu powder. Smaller the amount of Cu powder lower will be the production of discharge heat, and hence less amount of material eroded from the Al electrode [24]. At 1.5 g/100 ml C p , Sunflower oil gives the least value of EWR rate.…”

“…It was revealed that the BC 4 significantly enhances the corner accuracy (43.66%) and MRR (28.46%) compared to other three powders. Ramesh and Jenarthanan [24] optimized the control variables, namely, C p , I p , and duty factor, for material removal mechanism and surface finish in PM-EDM using kerosene dielectric, and three powders, namely, graphite, manganese (Mn), 6 and silicon (Si). 7 Their findings highlighted the supremacy of the graphite powder for MRR and silicon powder for surface finish.…”

Effects of process parameters on fatigue behavior and surface integrity of tool steel produced by electrical discharge turning

“…Particularly in the electrical discharge turning, it is very difficult to regulate a single best set of machining parameters, as the parameters influence machinability indicators in many ways. In order to reach the mentioned solution, it should be done with multiple optimization methods for example the gray relational analysis approach [14,15].…”

Análisis de la integridad de la superficie y evaluación de la sostenibilidad en el mecanizado por electroerosión de un material compuesto de matriz metálica ingenieril Al-22% SiC

“…Experimental methodologies, modeling, and optimization techniques play a critical role in such situations. For this reason, researchers have approached various statistical (Yildiz et al, 2012;Sivam et al, 2013;Uyyala et al, 2014;Gohil and Puri, 2016;Singh et al, 2019;Chaudhury and Samantaray, 2021) and computational (Bharathi Raja et al, 2013;Garcia Rojas et al, 2013;Cakir et al, 2013;Mohanty et al, 2017;Sahu et al, 2020;Ramesh and Jenarthanan, 2021) methods in their works for predictive modelling (Puertas and Perez, 2003;Keskin et al, 2005;Markopoulos et al, 2008;Gao et al, 2008;Chattopadhyay et al, 2009;Pradhan et al, 2009;Shabgard et al, 2009;Gopalakannan and Senthilvelan, 2013;Yadav and Yadava, 2014;Kumar et al, 2014;Majumder, 2014;Talla et al, 2015;Selvarajan et al, 2017;Belloufi et al, 2020), parametric optimization (Singh, 2012;Bharti et al, 2012;Tang and Guo, 2013;Tang and Du, 2014;Prabhu et al, 2013;Tzeng and Chen, 2013;Reddy et al, 2014;Singh et al, 2015;Marichamy et al, 2016;Prakash et al, 2016;R...…”